{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-8hjwN6W0VJz"
      },
      "source": [
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/Trusted-AI/AIF360/blob/main/examples/sklearn/demo_mdss_bias_scan.ipynb)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TMH4ugRk0JiK"
      },
      "source": [
        "# Identifying Significant Predictive Bias in Classifiers\n",
        "\n",
        "In this notebook, we attempt to recreate the analysis by Zhe Zhang and Daniel Neill in [Identifying Significant Predictive Bias in Classifiers](https://arxiv.org/pdf/1611.08292.pdf).\n",
        "\n",
        "The analysis is broken down into three steps, starting with a model trained on COMPAS decile scores only. After running bias scan, we add the distinguishing feature, priors count, to the model. We scan again and train a third model with the new subgroups accounted for. Finally, we reproduce Figure 2 from the paper."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cNtZqZph0LDC"
      },
      "outputs": [],
      "source": [
        "# Install AIF360\n",
        "!pip install 'aif360'"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "muU69Pg20JiY"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "sns.set(context='talk', style='whitegrid')\n",
        "\n",
        "from sklearn.metrics import RocCurveDisplay\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "\n",
        "from aif360.sklearn.datasets import fetch_compas\n",
        "from aif360.sklearn.detectors import bias_scan\n",
        "from aif360.sklearn.metrics import mdss_bias_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "p9UbLZxv0Jid"
      },
      "source": [
        "Data loading"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "id": "fGqb0ZGc0Jie",
        "outputId": "f090b129-0d8d-4573-e77d-69489bc16874"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>sex</th>\n",
              "      <th>race</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>priors_count</th>\n",
              "      <th>c_charge_degree</th>\n",
              "      <th>decile_score</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>race</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"9\" valign=\"top\">Male</th>\n",
              "      <th>Other</th>\n",
              "      <td>Male</td>\n",
              "      <td>Other</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>African-American</th>\n",
              "      <td>Male</td>\n",
              "      <td>African-American</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>African-American</th>\n",
              "      <td>Male</td>\n",
              "      <td>African-American</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>1 to 5</td>\n",
              "      <td>F</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>Male</td>\n",
              "      <td>Other</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Caucasian</th>\n",
              "      <td>Male</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>More than 5</td>\n",
              "      <td>F</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>African-American</th>\n",
              "      <td>Male</td>\n",
              "      <td>African-American</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>African-American</th>\n",
              "      <td>Male</td>\n",
              "      <td>African-American</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>Male</td>\n",
              "      <td>Other</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">Female</th>\n",
              "      <th>African-American</th>\n",
              "      <td>Female</td>\n",
              "      <td>African-American</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>1 to 5</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Hispanic</th>\n",
              "      <td>Female</td>\n",
              "      <td>Hispanic</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>1 to 5</td>\n",
              "      <td>F</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>6172 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                            sex              race          age_cat  \\\n",
              "sex    race                                                          \n",
              "Male   Other               Male             Other  Greater than 45   \n",
              "       African-American    Male  African-American          25 - 45   \n",
              "       African-American    Male  African-American     Less than 25   \n",
              "       Other               Male             Other          25 - 45   \n",
              "       Caucasian           Male         Caucasian          25 - 45   \n",
              "...                         ...               ...              ...   \n",
              "       African-American    Male  African-American     Less than 25   \n",
              "       African-American    Male  African-American     Less than 25   \n",
              "       Other               Male             Other  Greater than 45   \n",
              "Female African-American  Female  African-American          25 - 45   \n",
              "       Hispanic          Female          Hispanic     Less than 25   \n",
              "\n",
              "                        priors_count c_charge_degree  decile_score  \n",
              "sex    race                                                         \n",
              "Male   Other                       0               F             1  \n",
              "       African-American            0               F             3  \n",
              "       African-American       1 to 5               F             4  \n",
              "       Other                       0               M             1  \n",
              "       Caucasian         More than 5               F             6  \n",
              "...                              ...             ...           ...  \n",
              "       African-American            0               F             7  \n",
              "       African-American            0               F             3  \n",
              "       Other                       0               F             1  \n",
              "Female African-American       1 to 5               M             2  \n",
              "       Hispanic               1 to 5               F             4  \n",
              "\n",
              "[6172 rows x 6 columns]"
            ]
          },
          "execution_count": 2,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "cols = ['sex', 'race', 'age_cat', 'priors_count', 'c_charge_degree', 'decile_score']\n",
        "X, y = fetch_compas(usecols=cols)\n",
        "# Quantize priors count between 0, 1-5, and >5\n",
        "X['priors_count'] = pd.cut(X['priors_count'], [-1, 0, 5, 100],\n",
        "                           labels=['0', '1 to 5', 'More than 5'])\n",
        "X"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TNU-3kNO0Jif"
      },
      "source": [
        "### 1. Decile score only"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "V-oGf_Kr0Jig",
        "outputId": "b80bbe35-bae8-47f9-c6a4-0849d2f3994a"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 432x432 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "dec = X[['decile_score']]\n",
        "northpointe = LogisticRegression(penalty='none').fit(dec, y)\n",
        "y_prob = northpointe.predict_proba(dec)[:, 1]\n",
        "y_prob = pd.Series(y_prob, name='recid_prob', index=X.index)\n",
        "\n",
        "f, ax = plt.subplots(figsize=(6, 6))\n",
        "RocCurveDisplay.from_estimator(northpointe, dec, y, ax=ax);"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "FdGrKX300Jig"
      },
      "outputs": [],
      "source": [
        "df = pd.concat([X, 1-y_prob], axis=1)\n",
        "orig_clf = df.groupby('decile_score').recid_prob.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0jy-EJU80Jih"
      },
      "source": [
        "#### Privileged group\n",
        "\n",
        "\"Privileged\" in this case means the model underestimates the probability of recidivism (overestimates favorable outcomes) for this subgroup. This leads to advantage for those individuals."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "9okV_MYh0Jii",
        "outputId": "7d43e67c-99f9-4c96-dbe4-4ba7edb9f762"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "({'priors_count': ['More than 5']}, 36.3302)"
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "priv_sub, priv_score = bias_scan(X, y, y_prob, pos_label='Survived',\n",
        "                                 penalty=0.5, overpredicted=True)\n",
        "priv = df[priv_sub.keys()].isin(priv_sub).all(axis=1)\n",
        "priv_sub, priv_score"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uwyQpJUq0Jij"
      },
      "source": [
        "Note: we show probabilities of recidivism but bias scanning is done with respect to the positive label, 'Survived'."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "id": "7Twg3LBh0Jij",
        "outputId": "5b9dd4fa-cb5a-4f15-fee6-f494e59604ed"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Observed: 71.42%\n",
            "Expected: 60.27%\n",
            "n = 1221\n"
          ]
        }
      ],
      "source": [
        "print(f'Observed: {y[priv].cat.codes.mean():.2%}')\n",
        "print(f'Expected: {df[priv].recid_prob.mean():.2%}')\n",
        "print(f'n = {sum(priv)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g68mpYg20Jij"
      },
      "source": [
        "#### Unprivileged group\n",
        "\n",
        "\"Unprivileged\" means the model overestimates the probability of recidivism (underestimates favorable outcomes) for this subgroup. This disadvantages those individuals."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "TcmWF0oE0Jik",
        "outputId": "4797e9af-8df2-43b4-ae7e-50cd36830a3b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "({'priors_count': ['0']}, 45.1434)"
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unpriv_sub, unpriv_score = bias_scan(X, y, y_prob, pos_label='Survived',\n",
        "                                     penalty=0.5, overpredicted=False)\n",
        "unpriv = df[unpriv_sub.keys()].isin(unpriv_sub).all(axis=1)\n",
        "unpriv_sub, unpriv_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "i5YcmHkL0Jik",
        "outputId": "15340c08-41c7-4043-9800-75b416891a00"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Observed: 28.63%\n",
            "Expected: 38.06%\n",
            "n = 2085\n"
          ]
        }
      ],
      "source": [
        "print(f'Observed: {y[unpriv].cat.codes.mean():.2%}')\n",
        "print(f'Expected: {df[unpriv].recid_prob.mean():.2%}')\n",
        "print(f'n = {sum(unpriv)}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WyjOYZuK0Jil"
      },
      "source": [
        "### 2. Decile score + priors count"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "ECQzsBEJ0Jil"
      },
      "outputs": [],
      "source": [
        "dec = dec.assign(priors_count=X['priors_count'].cat.codes)\n",
        "northpointe = LogisticRegression(penalty='none').fit(dec, y)\n",
        "y_prob_pc = northpointe.predict_proba(dec)[:, 1]\n",
        "y_prob_pc = pd.Series(y_prob_pc, name='recid_prob', index=X.index)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "soKsVOlK0Jim"
      },
      "outputs": [],
      "source": [
        "df = pd.concat([X, 1-y_prob_pc], axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "O_yFQy-00Jim"
      },
      "source": [
        "#### Privileged group"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "LUDfrmiG0Jim",
        "outputId": "3a4c7e5b-ade8-4083-bfb8-2145d5c5cffb"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "({'sex': ['Male'], 'age_cat': ['Less than 25']}, 24.49)"
            ]
          },
          "execution_count": 11,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "priv_sub, priv_score = bias_scan(X, y, y_prob_pc, pos_label='Survived',\n",
        "                                 penalty=1, overpredicted=True)\n",
        "priv = df[priv_sub.keys()].isin(priv_sub).all(axis=1)\n",
        "priv_sub, priv_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "9QT8YdEY0Jin",
        "outputId": "39c3d98d-dbe7-4b9c-e37f-d77171ff4b3a"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Observed: 60.04%\n",
            "Expected: 49.76%\n",
            "n = 1101\n",
            "unpenalized score: 26.49\n"
          ]
        }
      ],
      "source": [
        "print(f'Observed: {y[priv].cat.codes.mean():.2%}')\n",
        "print(f'Expected: {df[priv].recid_prob.mean():.2%}')\n",
        "print(f'n = {sum(priv)}')\n",
        "\n",
        "priv_unpen = mdss_bias_score(y, y_prob_pc, X=X, subset=priv_sub,\n",
        "                             pos_label='Survived', overpredicted=True, penalty=0)\n",
        "print(f'unpenalized score: {priv_unpen:.2f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TyfxvmaI0Jin"
      },
      "source": [
        "#### Unprivileged group"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "ymoBNKwu0Jin",
        "outputId": "c9fa465a-560b-413d-abe2-3155ce2eab69"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "({'decile_score': [2, 3, 6, 9, 10],\n",
              "  'sex': ['Female'],\n",
              "  'c_charge_degree': ['M']},\n",
              " 12.1591)"
            ]
          },
          "execution_count": 13,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "unpriv_sub, unpriv_score = bias_scan(X, y, y_prob_pc, pos_label='Survived',\n",
        "                                     penalty=0.25, overpredicted=False, num_iters=25)\n",
        "unpriv = df[unpriv_sub.keys()].isin(unpriv_sub).all(axis=1)\n",
        "unpriv_sub, unpriv_score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "zFRvNXCH0Jio",
        "outputId": "2d54ade4-6b77-481f-d21f-59cdeb572580"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Observed: 20.79%\n",
            "Expected: 36.96%\n",
            "n = 202\n",
            "unpenalized score: 13.91\n"
          ]
        }
      ],
      "source": [
        "print(f'Observed: {y[unpriv].cat.codes.mean():.2%}')\n",
        "print(f'Expected: {df[unpriv].recid_prob.mean():.2%}')\n",
        "print(f'n = {sum(unpriv)}')\n",
        "\n",
        "unpriv_unpen = mdss_bias_score(y, y_prob_pc, X=X, subset=unpriv_sub,\n",
        "                               pos_label='Survived', overpredicted=False, penalty=0)\n",
        "print(f'unpenalized score: {unpriv_unpen:.2f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "H2PhWmK_0Jip"
      },
      "source": [
        "### 3. Decile score + priors count + top groups"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "id": "sKAh4tzZ0Jip",
        "outputId": "7bdba24a-d496-4dba-e137-22d82ab95b45"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th>sex</th>\n",
              "      <th>race</th>\n",
              "      <th>age_cat</th>\n",
              "      <th>priors_count</th>\n",
              "      <th>c_charge_degree</th>\n",
              "      <th>decile_score</th>\n",
              "      <th>recid_prob</th>\n",
              "      <th>group</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>sex</th>\n",
              "      <th>race</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">Male</th>\n",
              "      <th>Other</th>\n",
              "      <td>Male</td>\n",
              "      <td>Other</td>\n",
              "      <td>Greater than 45</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>1</td>\n",
              "      <td>0.186358</td>\n",
              "      <td>neither</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>African-American</th>\n",
              "      <td>Male</td>\n",
              "      <td>African-American</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>0</td>\n",
              "      <td>F</td>\n",
              "      <td>3</td>\n",
              "      <td>0.265247</td>\n",
              "      <td>neither</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>African-American</th>\n",
              "      <td>Male</td>\n",
              "      <td>African-American</td>\n",
              "      <td>Less than 25</td>\n",
              "      <td>1 to 5</td>\n",
              "      <td>F</td>\n",
              "      <td>4</td>\n",
              "      <td>0.448002</td>\n",
              "      <td>under-estimated</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Other</th>\n",
              "      <td>Male</td>\n",
              "      <td>Other</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>0</td>\n",
              "      <td>M</td>\n",
              "      <td>1</td>\n",
              "      <td>0.186358</td>\n",
              "      <td>neither</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Caucasian</th>\n",
              "      <td>Male</td>\n",
              "      <td>Caucasian</td>\n",
              "      <td>25 - 45</td>\n",
              "      <td>More than 5</td>\n",
              "      <td>F</td>\n",
              "      <td>6</td>\n",
              "      <td>0.696115</td>\n",
              "      <td>neither</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                        sex              race          age_cat priors_count  \\\n",
              "sex  race                                                                     \n",
              "Male Other             Male             Other  Greater than 45            0   \n",
              "     African-American  Male  African-American          25 - 45            0   \n",
              "     African-American  Male  African-American     Less than 25       1 to 5   \n",
              "     Other             Male             Other          25 - 45            0   \n",
              "     Caucasian         Male         Caucasian          25 - 45  More than 5   \n",
              "\n",
              "                      c_charge_degree  decile_score  recid_prob  \\\n",
              "sex  race                                                         \n",
              "Male Other                          F             1    0.186358   \n",
              "     African-American               F             3    0.265247   \n",
              "     African-American               F             4    0.448002   \n",
              "     Other                          M             1    0.186358   \n",
              "     Caucasian                      F             6    0.696115   \n",
              "\n",
              "                                 group  \n",
              "sex  race                               \n",
              "Male Other                     neither  \n",
              "     African-American          neither  \n",
              "     African-American  under-estimated  \n",
              "     Other                     neither  \n",
              "     Caucasian                 neither  "
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df['group'] = 'neither'\n",
        "df.loc[priv, 'group'] = 'under-estimated'\n",
        "df.loc[unpriv, 'group'] = 'over-estimated'\n",
        "df['group'] = df.group.astype('category')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "FITyXnKz0Jiq"
      },
      "outputs": [],
      "source": [
        "dec = pd.concat([dec, pd.get_dummies(df.group)], axis=1)\n",
        "northpointe = LogisticRegression(penalty='none').fit(dec, y)\n",
        "y_prob_pcg = northpointe.predict_proba(dec)[:, 1]\n",
        "df['recid_prob'] = 1 - y_prob_pcg"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "HlB5K8ps0Jiq",
        "outputId": "262e523a-0ee6-4bf3-e75a-1de1cc8dfb1d"
      },
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1227.9x360 with 3 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "p = sns.relplot(data=df.groupby(['decile_score', 'priors_count', 'group']).mean(numeric_only=True),\n",
        "                x='decile_score', y='recid_prob', hue='priors_count',\n",
        "                style='priors_count', palette=['r', 'g', 'b'],\n",
        "                markers=['o', 's', '^'], col='group', s=250)\n",
        "for ax in p.axes.flatten():\n",
        "    ax.plot(range(1, 11), orig_clf, '--k')\n",
        "plt.ylim([0, 1]);\n",
        "plt.yticks(np.linspace(0, 1., 5));\n",
        "plt.xticks(range(1, 11));"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3.9.7 ('aif360')",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.7"
    },
    "orig_nbformat": 4,
    "vscode": {
      "interpreter": {
        "hash": "d0c5ced7753e77a483fec8ff7063075635521cce6e0bd54998c8f174742209dd"
      }
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}
